Hadoop at Yahoo
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Operations Engineer Savitha Ravikrishnan - Site Reliability Engineer Apache Hadoop at Yahoo is a massive platform with 36 different clusters spread across YARN, Apache HBase, and Apache Storm deployments, totaling 60, servers made up of s of different hardware configurations accumulated over generations, presenting unique operational challenges and a variety of unforeseen corner cases.
Software Engineer Huibing Yin - Sr.
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Director, Products With a long history of open innovation with Hadoop, Yahoo continues to invest in and expand the platform capabilities by pushing the boundaries of what the platform can accomplish for the entire organization. In the last 11 years yes, it is that old!
In this talk, we explore what makes the shared multi-tenant Hadoop platform so special at Yahoo. We released CaffeOnSpark as an open source project in earlyand shared its architecture design and basic usage at Hadoop Summit In this talk, we will update audiences about the recent development of CaffeOnSpark. We will highlight new features and capabilities: The Apache Tez Shuffle Handler adds composite fetch which has support for multi-partition fetch to mitigate performance slow down and provides deletion APIs to reduce disk usage for long running Tez sessions.
As an emerging technology we will outline future roadmap for the Apache Tez Shuffle Handler and provide performance evaluation results from real world jobs at scale.
Principal Engineer At Yahoo! HBase has been running as a hosted multi-tenant service since In a single HBase cluster we have around 30 tenants running various types of workloads ie batch, near real-time, ad-hoc, etc. We will walk through multi-tenancy features explaining our motivation, how they work as well as our experiences running these multi-tenant clusters.
These features will be available in Apache HBase 2. Principal Engineer, Yahoo Mail Sincethe Yahoo Mail Data Engineering team took on the task of revamping the Mail data warehouse and analytics infrastructure in order to drive the continued growth and evolution of Yahoo Mail. Along the way we have built a 50 PB Hadoop warehouse, and surrounding analytics and machine learning programs that have transformed the way data plays in Yahoo Mail.
In this session we will share our experience from this 3 year journey, from the system architecture, analytics systems built, to the learnings from development and drive for adoption. Increasingly, we are investigating the practicality of applying Hive to real-time queries, such as those generated by interactive BI reporting systems.
In order for Hive to succeed in this space, it must be performant in all aspects of query execution, from query compilation to job execution.
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One such component is the interaction with the underlying database at the core of the Metastore. As an alternative to ObjectStore, we created OracleStore as a proof-of-concept. Freed of the restrictions imposed by DataNucleus, we were able to design a more performant database schema that better met our needs.
Then, we implemented OracleStore with specific goals built-in from the start, such as ensuring the deduplication of data. In this talk we will discuss the details behind OracleStore and the gains that were realized with this alternative implementation.
This seemed to be ample time for a piece to blossom. A source was determined and then a word was found, a Utility Member was allowed to decide if there should be a second word in the title.
We took turns having authority. If a second word was desired, the process would then be repeated. We set out to create music similar to that which we enjoy playing, so we imposed existing song structures upon most of the songs.
Six different existing song templates were used, three written by Utility Member K and three written by Utility Member D. It was decided that twenty of the thirty songs should originate from the structures, one would have only rests and the remaining nine would consist of songs with a randomly determined number of notes between one and six. The songs were then assigned a randomly determined structure by rolling dice.
To simplify the process, it was decided that we leave minors, sharps, flats and sevenths in place within a given song structure, so the only thing to be replaced would be the root note. In the nine songs without structure, a coin flip would be used to determine whether the song would be major or minor.
The dice were then rolled. We used the six points on the die to represent the notes A-G. Since there are seven notes, we eliminated from the possibilities the note that had just appeared, i. This process was repeated throughout the twenty songs with structures. The same principle was applied to those without structure: We had also decided that the instrumentation would be what was familiar to us: The drums were to be recorded first, and it was apparent that certain lasting song elements would be determined by the instrument recorded next, be it guitar or bass.
So coin flips decided which would come first. A key operating principal throughout the project was that the dice would be used as a songwriting and song-shaping device, and that to serve the pieces properly, the dice would stay out of certain performance and technical aspects. This is where we break, perhaps, from a hardcore random aesthetic.